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Studyguide Detailed for First Exam

by: Natalie Land

Studyguide Detailed for First Exam STC 103

Marketplace > University of Miami > Communications > STC 103 > Studyguide Detailed for First Exam
Natalie Land
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Detailed study guide for chapters 1 and 2 plus how to do equations! for our exam!
Statistical Reasoning for Strategic Communication
Bo Ra Yook
Study Guide
Math, STC103, STC, strategy, Advertising, strategical, reasoning
50 ?




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This 8 page Study Guide was uploaded by Natalie Land on Tuesday September 13, 2016. The Study Guide belongs to STC 103 at University of Miami taught by Bo Ra Yook in Fall 2016. Since its upload, it has received 9 views. For similar materials see Statistical Reasoning for Strategic Communication in Communications at University of Miami.

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Date Created: 09/13/16
STC  103   Statistical  Reasoning     •   What  is  research?   o   Systematic  investigation  of  phenomena  that  leads  us  to  understand  and  predict   outcomes     §   Academic  research:  guided  by  a  theory,  select  methodology,  how  will   they  analyze  data,  academic  research  always  tests  hypothesis:  results   plus  discussions     §   Begin  with  a  problem  question  or  purpose     •   How  many  people  are  attending  class?   •   What’s  a  popular  class?   §   Relational:  something’s  are  related  but  not  necessarily  X  causes  Y   §   Casual:  X  does  cause  Y   §   Research  question:  statement  of  the  problem     •   What  is  the  most  popular  auto  brand?  (descriptive)  no  relation  of   X  and  Y     •   Females  are  more  likely  to  purchase  white  car  than  male   (relational)   §   Hypothesis:  testable  prediction,  based  on  theory  or  observation   (relational)     §   Research  method:  strategy  plan  and  activity  to  accomplish  research     •   How  to  test  hypothesis   •   Quantitative:  objective,  systematic,  controlled,     o   Can  be  generalized,  uses  numbers  and  discrete  units     o   Descriptive:  seeing  things  as  they  are,  no  manipulation,  made  of  surveys  or  polls     o   Experimental:  manipulation  to  see  if  something  different  occurs     o   Why  quantitative  rather  than  qualitative:  because  social  science  we  want  to   replicate  in  different  target  audience     §   Apply  findings  to  different  studies     o   Choose  method  based  on  research  objective  and  what  you  want  to  know     o   UNDERSTAND  ADVANTAGES  VS  DISADVANTAGES     o   Phenomena:  any  object  or  event  explained  how  will  you  explain  the  event  or   whats  going  on   •   Qualitative:  specific  to  case,  each  case  is  unique,  cannot  be  generalized     o   Ie  :  freshman  are  more  international  students  from  Europe  and  how  those  feel   are  different  from  Africa     •   Variable:  observable  characteristic     o   Independent  variable:  the  variable  that  represents  the  cause  of  the  dependent   variable     §   Naturally  occurring     §   Age,  biological  sex   §   What  we  manipulate     §   Color  credibility     §   Iv  causes  an  effect  (DV)   o   DV:  outcome,  the  effect  of  the  independent  variable     §   Perceived  reputation   §   Variable  the  research  tries  to  explain   •   Behavioral:  likes,  retweets,  number  of  calls  your  getting     •   Attidunal:  brand  preference,  trust,  relationship,  reputation   o   Iv  causes  the  DV     o   IV  will  cause  the  DV  the  effect     o   When  people  like  the  brand  (IV)  the  more  likely  they  are  to  purchase  (DV)   o       •   What  is  Data?   o   Data:  reports  of  observation  of  variables     o   Data  reflect  something  measured     §   It  has  no  meaning  until  researchers  create  a  meaningful  system  for   interpretation     §   Has  to  be  measured     o   Has  to  be  reliable  and  valid     §   Reliable:  same  results  over  time:  consistent     §   Valid:  measuring  what  we  want  to  measure,  describing  what  we  think  it  is     •   Statistics:  a  branch  of  applied  math  that  specializes  in  procedures  for  describing  and   reasoning  from  measures     o   A  lot  of  procedures  each  with  mathematical  deductions     o   What  is  a  measurement?  Links  observation  to  numbers     o   Data:  plural:  datum:  single  observation   o   Social  science:  1  to  5  scale  or  1  to  10  scale:  very  reliable  and  valid  results     •   Physical  observation:  such  as  objective  amounts     o   Advertising:  cost  per  thousand,  placement  on  a  page  or  impressions     •   Psychological:  cant  be  seen  :  values,  beliefs,  attitudes     •   Concepts  that  we  don’t  know  how  to  calculate:  so  we  come  up  with  measurements     •   Stake  hold:  is  part  of  the  company  in  any  way,  employee,  etc     •   Stock  holder:  owns  a  stock  or  share  of  company     •   ROI:  return  on  investment:  money  value  but  also  expectation   •   Sampling   o   Large  numbers  (public,  audience)  that  are  too  large  to  get  data  on  so  we  use   sample  of  entire  population   §   We  want  to  know  the  entire  communication  school  students:  this  is   population   §   Sample:  just  one  class   o   Sample:  subset  of  population   •   Descriptive  statistics:  the  overall  population   •   Sampling:  calculated  values  that  represent  how  sample  characteristics  vary  from   population  characteristics     •   Probabilistic  sampling  statistics:  we  give  entire  population  same  opportunity  to  be  part   of  the  sample     o   All  names  in  one  bowl     •   Statistical  analysis:  decide  before  hand  what  procedure  used  and  what  criteria  involved   in  reasoning     o   Social  science  accepts  only  five  percent  error  aka  ninety  five  percent  confidence   in  outcome     •   Measurement   o   Scheme  for  assignment  of  numbers  or  symbols  to  specify  different   characteristics  of  a  variable     §   It’s  the  how     §   Links  observation  to  the  number     §   Standardized     o   Physical  observation:  such  as  objective  amounts     o   Psychological:  observations  that  can’t  be  seen     §   Perceptions,  influence  someone  to  buy  something     o   Role:  bridge  of  what’s  our  there  in  the  real  world  vs  interpret  that  into  our  study     §   Observation  plus  statistical  models     •   Two  classifications     o   Continuous:  time  spending,  how  likely     o   Categorical:  gender,  years  in  college,  category  information     •   Construct:  abstract  ideas  things  we  can’t  view     •   Conceptual:  verbal  meaning  of  the  concept     •   Operational:  translates  into  prescription  for  measurement     •   How  to  measure  variables   o   Using  scales,  scale:  specific  scheme  for  assigning  numbers  or  symbols  to   designate  characteristic  of  variables     o   Scale  example:  A+  A-­‐  these  are  for  grades     o   Behavioral  intention  measure:  something  you  want  to  plan  or  do     §   How  likely,  1,  2,3,4,5:  strongly  disagree  to  agree     •   Levels  of  measurement     o   Different  measurement  levels  offer  varying  degrees  of  exactness  in  describing   given  characteristics     •   Two  types  of  variables     o   Categorical:  nominal  or  ordinal     o   Continuous:  amount  of  time  or  money  etc     §   Interval,  or  ratio   o   Categorical:  nominal   §   Assignment  of  numbers  to  categories  into  math  meaning     •   Where  do  you  live?   •   (1)  on  campus     •   (2)  off  campus     •   the  numbers  don’t  mean  anything     o   ordinal   §   ordered  relations,  unspecified  intervals,  rank  ordering:  better,  faster,   stronger     §   IE:  best  selling  book  #1,  #2  etc     o   Interval:  to  identify  ordered  relations  of  characteristics,  equal  intervals,     o   Ratio  scale:  equal  distance  but  has  an  absolute  zero  point   §   Age,  distance,  time     o   Categorical:  places  observation  into  classes     §   No  values     §   Reprenstation  as  counts  or  percentages     §   Nominal  and  ordinal     •   Nominal:  numbers  don’t  matter     •   Ordinal:  numbers  do  mater     §   Set,  race,  major   •   Continuous:  places  observation  on  a  continuum   o   Mean,  median,  mode,  range     o   Interval,  scale,  tatio   o   Age,  income,  temperature     •   Continuous  can  be  categorical  but  categorical  cannot  be  continuous     •   Categorical:  nominal   •   Categorical:  ordinal   •   Independent  variable:  live  on  campus  or  not     •   Dependent  Variable:  Likely  to  purchase  a  car     August  31,  2016   •   Interval:  likely:  behavior,  thinking  process,  no  absolute  zero     •   Ratio:  absolute  zero   •   Measurement  level  is  nominal  ordinal  ratio  or  interval     •   Roadmap  to  measuring  behavior   o   Behavior  of  consumer     o   We  measure,  values,  beliefs,  attitudes,  opinions:  to  see  the  behavior     •   Attitude  measurement   o   Interval  data   o   Respondents  react  to  statements  by  degree  of  agreement     §   Must  have  midpoint   §   1  to  7,  1  to  5,  1  to  9   §   add  number  of  responses     §   two  or  more  statemenets     o   Sematic  Diffential/  Bipolar  Scale   §   Each  side  has  opposite  meaning     §   Good  bad   §   Easy  difficult   §   Helpful  unhelpful   •   Measurement  adequacy     o   Reliability:  internal/  external  consistence  of  measurement     o   Externally:  with  the  same  conditions  would  be  the  same  result?   o   Internally:  subparts  of  measurement  related  to  eachother     o   Temporal  stability:  stable  result  over  time     §   Ie:  temperature  of  a  fridge     o   Internal  consistency:  set  of  scale  gives  you  the  same  results   §   Likelihood  to  purchase     §   All  give  you  the  same  number  of  more  likely  to  less  likely     o   Four  items  related  to  eachother     o   External:  degree  to  which  scroes  on  measure  are  stable  over  time     §   Test  retest  correlation:  asseses  strength  of  relationship  between  the   same  groups  score  on  same  measure  at  two  or  more  points     •   Overtime  very  simiiar  results  at  same  time     §   Internal  consistency:  degree  to  which  the  statements  all  tap  on  the  same   thing     §   Cronback  alpha:  asseses  internal  consisteny  of  the  items     •   From  0  to  1:  .8  is  most  desirable     o   To  increase  internal  consistency     §   Avoid  item  scales     •   Entire  measure  unreliable     •   Adequate  number  of  similiarly  worded  items   §   Reliability:  same  result  over  time     §   Validity:  something  measured  that  measures  what  you  want  to  measure     •   Ie  if  you  measure  temperature  or  want  to  but  instead  get  the   heart  rate  its  not  valid     §   To  be  useful  must  be  both  reliable  and  valid     §   Validity  implies  reliability  but  reliability  not  necessarily  validity.           Chapter  2  STC  103  Notes     •   Descriptive  Statistics:  to  describe,  summarize,  and  organize     o   Just  explaining  what’s  going  on     o   Sampling:  studying  and  testing  prediction  and  hypothesis     §   Experimental     o   Categorical  data:  either  or  classification  of  categories     o   Continuous  data:  continues,  could  be  infinite     §   Nominal,   •   Gender  numbers  don’t  mean  anything   §   ordinal,   •   order  matters   §   interval  ratio   •   how  likely     •   no  absolute  zero     §   ratio   •   absolute  zero     •   Frequency:  number  of  times  something  happens     o   Ie  :  in  one  week  I  eat  four  times  at  subway     o   To  see  the  frequency  distribution,  we  have  an  organized  table,  table  or  graph,   shows  the  categories,  and  the  frequency:  (number  of  individuals  in  each   category)     §   Valid  percentage  of  all  valid  cases,  if  a  variable  is  missing  not  valid     •   Bar  Chart     o   X  axis:  has  the  categories     o   Y  axis:  frequencies  for  each  category     •   Pie  Chart:   o   All  together  is  one  hundred  percent     •   Continuous  data  descriptions     o   Frequencies:  the  normal  curve     o   Measure  of  central  tendency:  how  data  is  spread  or  concentrated     o   Point  is  plotted  above  each  score  or  measurement  and  then  you  connect  the   dots     §   Histogram:  horizontal  line  at  each  point,  corresponds  to  interval  of  scores     §   Frequency  polygon:  graph  representing  the  frequency  of  scores  in   smooth  curve  points  connected     •   In  histogram  one  score  connects  to  another  in  frequency  it   doesn’t     •   Bar  chart  is  for  categorical  data  and  histograms  is  for  continuous   data     •   Continuous  Data     o   How  graph  will  shape?   §   Symmetrical:  data  on  each  side  is  mirrored     §   Skewed  scores:  pile  up  on  one  side  and  tapper  off  on  other     §   Skewedness:  measure  of  asymmetry  if  tail  is  on  right  its  high  scores  this   equals  positive  skew     §     §   positive  skew     §   if  tail  is  on  opposite  side  it’s  a  negative  skew  and  it  means  low  scores     §   kurtosis:  height  of  the  curve:  the  peak     •   leptokurtic:  less  difference  higher  peak     •   platykurtic:  more  difference,  flatter  peak     •   Central  Tendency:     o   How  together  the  data  is  around  the  middle     o   How  scores  are  clustered     §   Spread  out  or  together     o   3  Ms     §   mean   §   mode     §   median   o   purpose:  find  score,  most  typical  or  best  representative  of  the  entire  group     o   mean:  average,  sum  of  scores  divided  by  number  of  sample     o   M  :  sample  mean     o   The  u:  population  mean   o   Median:  middle  point  middle  score     o   If  n  is  odd  identify  middle  score  and  if  n  is  even  you  average  the  middle  pair  to   find  median     o   What  if  there  is  a  lot  of  N  scores     §   if  N  is  odd     •   median  equals  N+1  divided  by  two     •   if  N  is  even   o   N/2  plus  N+2/2     o   Mode:  most  frequent  score     o   If  mean  is  bigger  than  median,  then  we  get  a  positive  skewed     o   If  mean  is  less  than  median:  negatively  skewed     o   Mean  is  good  for  the  sum  of  all  individuals  Ns  or  when  you  know  value  of  every   score   §   Not  good  for  extreme  scores,  ordinal  data,  nominal  or  skewed   distribution     o   Median  is  good  for     §   Skewed  distribution,  undetermined  values,  open  ended,  ordinal  data,     §   Not  good  for  nominal     •   Measures  of  dispersion   o   Variability  of  scores     o   Range:  highest  score  minus  the  lowest  score     §   Based  on  only  two  scores     o   Variability:  distance  of  spread  of  scores  or  distance  of  a  score  from  the  mean     §   Purpose:  to  describe  distribution     o   Most  important  measure  is  variance  and  standard  deviation   §   Standard  deviation:  how  far  individual  score  from  mean     §   Describes  if  scores  are  clustered  closely  around  mean  or  scattered     §   Variance  is  used  for  population  and  sample  is  S  squared  used  for  sample     o   Variance  is  the  sum  of  all  the  (individual  scores  –  the  average  squared)  divided   by  the  number  of  individual  numbers  n-­‐1     o   And  for  the  standard  deviation  its  just  the  square  root  of  the  variance     •   Sample  vs  Population   o   Population:  the  universe  of  objects     §   Public,  target  audience,  too  large  to  measure     o   Sample:  portion  of  that  population   §   Randomly  drawn     o   Sampling:  goal  is  to  generalize  provide  an  estimate  population   o   Random  probability  sampling:  each  individual  in  population  has  equal  chance  of   getting  chosen  for  the  sample     •   Statistic:  characteristic  of  a  sample     •   Parameter:  characteristic  of  a  population   •   Statistical  inference:  process  by  which  parameter  can  be  estimated     •   Population  distribution:  work  for  starbucks  and  want  to  know  if  UM  students  attitudes   toward  starbucks  so  we  measure  attitude  of  all  UM  students     •   Sample  distribution:  draw  a  sample  and  ask  attitude  of  only  100  of  5,000  students  for   example     •   Sampling  distribution:  we  want  to  study  population  but  sub  group  cant  explain  entire   population  so  we  do  man  samples  so  many  samples  of  100  through  5,000  students     •   Frequency:  distribution  tied  to  a  particular  number  of  observations  and  how  this   number  is  divided  among  different  categories     •   Proportion:  of  total  number  of  unites      


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